When Big Data Join Hands With Machine Learning Leads To Much Smarter Decision

Machine Learning and Big Data are the two popular terminologies and both are revolutionizing business operations and regularly providing lots of new opportunities. Machine Learning is presently more subject to practical, large-scale applications than it has ever been in past. Big data on the other hand, became a thing in 2013, after it was observed that 90 percent of the world's data was generated in past years.

The spate of data generation, therefore, became a challenge as well as an opportunity. As an opportunity, big data enables businesses to not grope in the dark but make wise real-time decisions by providing them with insights into various market situations and ensuring a better understanding of consumers' behaviors and preferences.

It's however noteworthy that big data by itself is of little value. To be useful, it has to be operated on by various analytical methods, many of which don't go beyond providing mere statistical insights. Machine learning comes in handy as it goes further to unveil the hidden potentials of big data by producing and implementing solutions to complex business problems.

Following are the ways by combining big data with machine learning has helped in improving business intelligence:

1. Customer Segmentation

It is not uncommon to find distinct groups each comprising individuals who share a wide range of similarities - within a business's customer base. In fact, discovering such groups is a crucial step every business should take.

Fortunately, machine learning clustering algorithms are perfect for achieving this kind of a segmentation. Many such algorithms are unsupervised in that they don't require special human direction to operate. Rather, an unsupervised clustering algorithm requires only data for exploration, so as to discover similarities and differences (where they exist), and come up with distinct clusters based on a number of features.

In 2009, Orbitz created a machine learning team to facilitate segmentation, among other reasons. Three years later, it discovered a pattern from the data at its disposal: Mac users were willing to spend as much as 30 percent more per night for hotel rooms, when compared to Windows users. This discovery made it swing into action in a way I'll touch on, shortly, as it obviously helped to lay the grounds for segmenting the business's customer base based on the relative propensity to pay for varying hotel types.

Your business can also harness the power of machine learning and big data to achieve segmentation. But, first, you need to discover whether segmentation holds any potential benefit for your organization. If you believe it does, then it will become necessary to invest heavily in data analytics, make your business machine learning ready, and then employ a machine learning team. As you'll soon see, machine learning will not only help to accurately and efficiently make sense of the data at your disposal, but also help to implement core business strategies.

2. Make Targeting More Effective:

Merely knowing that your customer base is composed of different groups doesn't cut it you have to devise means to cater to divergent needs.

On the other hand, it's sometimes necessary to view one's customer base as comprising different individuals with various preferences rather than a conglomeration of different groups. This perspective will make it more pragmatic to tailor products to each individual based on his or her specific behavior and perceived preferences. Again, machine learning, under the aegis of big data, facilitates this.

Google, for example, uses big data to better understand your preferences and combines it with complex (machine learning) algorithms to provide supposedly relevant results for every query you make. This is why your past choices end up impacting on some of the results you're shown.

Machine learning and big data are also breaking grounds in targeted advertising. Pixar, for example, targets its audience with different movie advertisements which are based on learned preferences. Netflix also estimates that its algorithms produce $1 billion a year in value from customer retention, thanks to the "Netflix addiction" which is mostly spurred by accurate recommendations fostered by both user and item-based collaborative filtering.

In other words, business owners need to understand that targeting consumers differently makes a lot of sense, and that machine learning makes personalization, which is key to providing a better user experience, possible. Say you run an ecommerce business, machine learning can help you personalize your ads so that people see only products that are most likely suited to their needs. This will definitely add an unobstrusive touch to your platform and may improve your bottom line by increasing sales and engendering customer retention. Again, the "Netflix addiction" speaks volumes about the potentials of machine learning-induced targeting.

3. Analyzing Predictive Analysis:

After gaining insight into consumer behavior from big data, you'll want to use machine learning to develop generalizations and thus make predictions regarding various business issues.

In other words, machine learning models can learn behavior patterns from data and determine how likely it is for a person or a set of people to take certain actions, such as subscribing for a service. This makes it possible to anticipate events and make futuristic decisions.

The American Express Company used big data to analyze and predict consumer behavior by learning from historical transactions. Through this, it was able to predict 24 percent of accounts in its Australian market that were about to close within four months. T-mobile also uses big data to predict consumer fluctuations. To make these kind of predictions, you must employ machine learning expertise to help grapple with your business's data. Classification algorithms are usually used as the foundation for such predictions.

4. Doing Risk Analysis and Regulation:

Big data enables machine learning models to extensively analyze and regulate risks. For fraud detection, American Express applies machine learning to analyze large historical datasets. In fact, the machine learning system is considered to differ from the previously existent fraud detection systems which included only manually created rules, and is better off because it's likely to improve with more data inputs. It also saves the company millions of dollars, said Bernard Marr.

Your business can also make use of machine learning to decrease financial irregularities. Many organizations are, in fact, developing systems to make the process easier. IBM, for example, provides financial institutions with a machine learning system on IBM z/OS in order to aid financial risk management. This system pays particular attention to credit scoring and is targeted at deducing credit worthiness which it uses to gauge risks. Employing machine learning models can go a long way in ensuring anti-money laundering compliance, detecting rouge trading and other trade anomalies, so it's best to not starve your business of these elements of sanity.

Machine learning and big data are presently gaining the attention they deserve, and there's no doubting that both depend on each other's strength. More importantly, both have consistently made major impacts on how we undertake business operations. This article revealed some four ways by which a combination of machine learning and big data, if applied, can be a fillip to business intelligence. It's therefore left to you to up your game as an entrepreneur.